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| def predict(data): | |
| try: | |
| image_input = data.get('image', None) | |
| if not image_input: | |
| return json.dumps({"error": "No image provided."}) | |
| print(f"Received image input: {image_input}") | |
| # Check if the input is a PIL Image type | |
| if isinstance(image_input, Image.Image): | |
| print(f"Image is already loaded as PIL Image: {image_input}") | |
| else: | |
| # Check if the input contains a base64-encoded string or URL | |
| if image_input.startswith("http"): # URL case | |
| try: | |
| response = requests.get(image_input) | |
| image = Image.open(BytesIO(response.content)) | |
| print(f"Fetched image from URL: {image}") | |
| except Exception as e: | |
| print(f"Error fetching image from URL: {e}") | |
| return json.dumps({"error": f"Error fetching image from URL: {e}"}) | |
| else: # Assuming it is base64-encoded image data | |
| try: | |
| image_data = base64.b64decode(image_input) | |
| image = Image.open(BytesIO(image_data)) | |
| print(f"Decoded base64 image: {image}") | |
| except Exception as e: | |
| print(f"Error decoding base64 image: {e}") | |
| return json.dumps({"error": f"Error decoding base64 image: {e}"}) | |
| # Apply transformations | |
| image = transform(image).unsqueeze(0) | |
| print(f"Transformed image tensor: {image.shape}") | |
| image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) | |
| with torch.no_grad(): | |
| outputs = model(image) | |
| predicted_class = torch.argmax(outputs, dim=1).item() | |
| print(f"Prediction output: {outputs}, Predicted class: {predicted_class}") | |
| if predicted_class == 0: | |
| return json.dumps({"result": "The photo you've sent is of fall army worm with problem ID 126."}) | |
| elif predicted_class == 1: | |
| return json.dumps({"result": "The photo you've sent is of a healthy maize image."}) | |
| else: | |
| return json.dumps({"error": "Unexpected class prediction."}) | |
| except Exception as e: | |
| print(f"Error processing image: {e}") | |
| return json.dumps({"error": f"Error processing image: {e}"}) | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.JSON(label="Input JSON"), | |
| outputs=gr.Textbox(label="Prediction Result"), | |
| live=True, | |
| title="Maize Anomaly Detection", | |
| description="Upload an image of maize to detect anomalies like disease or pest infestation. You can provide local paths, URLs, or base64-encoded images." | |
| ) | |
| # Launch the Gradio interface | |
| iface.launch(share=True, show_error=True) | |